Publication content
Esa Turkulainen
5/30/2022
Create datasets
# Load data on individual donations
load("./data/r02.fd.bd.all.rdata") # outputs an object called "output" into the environment
donations <- output
# We only want to look at first donation event values from each donor
donors <- donations %>%
group_by(donor) %>%
filter(date == min(date)) %>%
ungroup()
# Load FinDonor demographic data
load("./data/r02ds.donorData.rdata") # outputs an object called "output" into the environment
findonor <- output
# Combine the FinDonor datasets
FinDonor <- left_join(donors, findonor, by = "donor")
# Load THL data
# Sofie: thldalta.rdata contains all five THL cohorts, extract FINRISK97 and Health2000 from the others
load("./data/thldata.rdata")
FinRisk97 <- thldata$fr1997
Health2k <- thldata$h2000
# Remove leftovers
rm(output)
rm(thldata)
## Rename useful stuff
# Ferritin, Self-Reported Health
FinDonor <- rename(FinDonor, SRH = QR17, Menstruation = QR79, Age_float = Age, Age = age)
FinRisk97 <- rename(FinRisk97, Ferritin = FERRITIN, SRH = Q40, Gender = SUKUP, Menstruation = K129, Age = IKA)
Health2k <- rename(Health2k, Ferritin = FERRITIINI, SRH = BA01, Gender = SP2, Menstruation = BD03, Age = IKA2, Menopause = MENOP, APOB = NMR_APOB, APOA1 = NMR_APOA1)
# Make "useful stuff" conform with each other
FinDonor <- FinDonor %>%
mutate(SRH = case_when(SRH == "Excellent" ~ 1,
SRH == "Very_good" ~ 2,
SRH == "Good" ~ 3,
SRH == "Satisfactory" ~ 4,
SRH == "Poor" ~ 5),
Group = case_when(Gender == "Men" ~ "Men",
Gender == "Women" & (Menstruation == "regular_period" | Menstruation == "irregular_period") ~ "Women|Menstr",
Gender == "Women" & Menstruation == "no_period" ~ "Women|Non-menstr",
TRUE ~ "NA")) # Equates to "else"
FinRisk97 <- FinRisk97 %>%
mutate(Gender = case_when(Gender == 1 ~ "Men",
Gender == 2 ~ "Women",
TRUE ~ "NA"),
Group = case_when(Gender == "Men" ~ "Men",
Gender == "Women" & (Menstruation == 1 | Menstruation == 2) ~ "Women|Menstr",
Gender == "Women" & Menstruation == 3 ~ "Women|Non-menstr",
TRUE ~ "NA"))
Health2k <- Health2k %>%
mutate(Gender = case_when(Gender == 1 ~ "Men",
Gender == 2 ~ "Women",
TRUE ~ "NA"),
# It is worth noting here, that menstruation status was not asked from women over 55. So, we treat these as postmenopausal.
Group = case_when(Gender == "Men" ~ "Men",
Gender == "Women" & (Menstruation == 1 | Menstruation == 2) ~ "Women|Menstr",
Gender == "Women" & (Menstruation == 3 | Age >= 55) ~ "Women|Non-menstr",
TRUE ~ "NA"))
# Donation eligibility
# These are both "approximates" in a sense, we don't have all the necessary variables to
# filter thoroughly, and we'll be able to do more filtering on Health2000 than FinRisk97
donor_eligible_h2k <- Health2k %>%
filter(BMII_PAINO.x >= 50 | BMII_PAINO.x <= 200) %>% # Filter away people <50kg and >200kg
filter(Age >= 18 | Age <= 66) %>% # Filter away too young and too old
filter((B_Hb >= 125 & Gender == "Women") | (B_Hb >= 135 & Gender == "Men")) %>% # Filter by hemoglobin
filter(BA08 == 0) %>% # filter out people with heart attacks
filter(BA09 == 0) %>% # filter out people with angina
filter(BA10 == 0) %>% # cardiac insufficiency / heart failure
filter(!(BA26 == 1 & ATC_A10A == 1)) %>% # filter out people who are diabetic AND use insulin
filter(SRH < 4) %>% # filter out "Bad" or "Very bad" SRH
rename(GlycA = GP) %>% # rename for ease of use
mutate(HbA1C = B_GHb_A1C * 10.93 - 23.50)
donor_eligible_fr <- FinRisk97 %>%
filter(PAINO >= 50 | PAINO <= 200) %>% # Filter away people <50kg and >200kg
filter(Age >= 18 | Age <= 66) %>% # Filter away too young and too old
#filter((HGB >= 125 & Gender == 2) | (HGB >= 135 & Gender == 1)) %>% # DON'T filter by hemoglobin, < 500 values in data
filter(Q15A != 2) %>% # STEMI, NSTEMI
filter(Q16A != 2) %>% # Stroke
# filter(Q38 != 2 & Q38 != 4) %>% # Insulin treatment (2: just insulin, 4: insulin and a tablet)
filter(Q17B != 2) %>% # cardiac insufficiency
filter(Q17C != 2) %>% # angina pectoris
filter(SRH < 4) %>% # filter out "Bad" or "Very bad" SRH
rename(GlycA = GP) # rename for ease of use
# Create useful mastersets
# fer_srh <- bind_rows(FinRisk97 = donor_eligible_fr[, c("Ferritin", "SRH", "Group")],
# Health2k = donor_eligible_h2k[, c("Ferritin", "SRH", "Group")], .id = "Cohort") %>%
# mutate(Group = ordered(Group, levels = c("Women|Menstr", "Women|Non-menstr", "Men")),
# Cohort = ordered(Cohort, levels = c("FinRisk97", "Health2k")),
# SRH = ordered(SRH, levels = 1:5)) %>%
# filter(Group != "NA") %>%
# drop_na()
fer_crp <- bind_rows(FinRisk97 = donor_eligible_fr[, c("Ferritin", "Group", "CRP")],
Health2k = donor_eligible_h2k[, c("Ferritin", "Group", "CRP")], .id = "Cohort") %>%
mutate(Group = ordered(Group, levels = c("Women|Menstr", "Women|Non-menstr", "Men")),
Cohort = ordered(Cohort, levels = c("FinRisk97", "Health2k"))) %>%
filter(Group != "NA") %>%
filter(CRP >= 0.01) %>%
drop_na()Mock Cohort|Var Tables
table1 <- as.data.frame(table(fer_crp$Group, fer_crp$Cohort))
table1$CRP <- c(paste0(round(summary(fer_crp$CRP[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_crp$CRP[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_crp$CRP[fer_crp$Group == "Men" & fer_crp$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_crp$CRP[fer_crp$Group == "Men" & fer_crp$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_crp$CRP[fer_crp$Group == "Men" & fer_crp$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_crp$CRP[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_crp$CRP[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_crp$CRP[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_crp$CRP[fer_crp$Group == "Men" & fer_crp$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_crp$CRP[fer_crp$Group == "Men" & fer_crp$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_crp$CRP[fer_crp$Group == "Men" & fer_crp$Cohort == "Health2k"])[5], 2), ")"))
table1$FER <- c(paste0(round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_crp$Ferritin[fer_crp$Group == "Men" & fer_crp$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_crp$Ferritin[fer_crp$Group == "Men" & fer_crp$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_crp$Ferritin[fer_crp$Group == "Men" & fer_crp$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Menstr" & fer_crp$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_crp$Ferritin[fer_crp$Group == "Women|Non-menstr" & fer_crp$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_crp$Ferritin[fer_crp$Group == "Men" & fer_crp$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_crp$Ferritin[fer_crp$Group == "Men" & fer_crp$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_crp$Ferritin[fer_crp$Group == "Men" & fer_crp$Cohort == "Health2k"])[5], 2), ")"))
table1## Var1 Var2 Freq CRP FER
## 1 Women|Menstr FinRisk97 1980 0.76 | (0.38, 1.85) 23.97 | (12.35, 42.51)
## 2 Women|Non-menstr FinRisk97 1001 1.26 | (0.61, 2.6) 55.8 | (31.42, 94.74)
## 3 Men FinRisk97 2801 0.91 | (0.47, 1.92) 110.99 | (65.87, 181.3)
## 4 Women|Menstr Health2k 976 0.61 | (0.26, 1.78) 27.9 | (15.17, 48.54)
## 5 Women|Non-menstr Health2k 785 1.02 | (0.4, 2.35) 55.97 | (32, 95.1)
## 6 Men Health2k 1793 0.79 | (0.35, 1.77) 123.1 | (76.32, 192.7)Exclusions: Health2000
# ExclusionTable method
excl_h2k_women_mens <- exclusion_table(data = Health2k,
inclusion_criteria = c("Group == 'Women|Menstr'"),
exclusion_criteria = c("BMII_PAINO.x < 50", "BMII_PAINO.x > 200", "Age < 18", "Age > 66", "SRH >= 4",
"B_Hb < 125", "BA08 == 1", "BA09 == 1", "BA10 == 1", "BA26 == 1 & ATC_A10A == 1"),
keep_data = TRUE)
excl_h2k_women_mens##
## ======================================================
## Excluded the following observations:
## ======================================================
## Exclusions based on INCLUSION criteria
##
## inclusion n_prior n_post n_excluded
## 1 Group == 'Women|Menstr' 6264 1508 4756
## 2 TOTAL 6264 1508 4756
##
## Exclusions based on EXCLUSION criteria
##
## exclusion n_prior n_post n_excluded
## 1 BMII_PAINO.x < 50 1508 1459 49
## 2 BMII_PAINO.x > 200 1459 1459 0
## 3 Age < 18 1459 1459 0
## 4 Age > 66 1459 1459 0
## 5 SRH >= 4 1459 1409 50
## 6 B_Hb < 125 1409 1115 294
## 7 BA08 == 1 1115 1114 1
## 8 BA09 == 1 1114 1113 1
## 9 BA10 == 1 1113 1111 2
## 10 BA26 == 1 & ATC_A10A == 1 1111 1105 6
## 11 TOTAL 1508 1105 403
##
## ======================================================excl_h2k_women_nonmens <- exclusion_table(data = Health2k,
inclusion_criteria = c("Group == 'Women|Non-menstr'"),
exclusion_criteria = c("BMII_PAINO.x < 50", "BMII_PAINO.x > 200", "Age < 18", "Age > 66", "SRH >= 4",
"B_Hb < 125", "BA08 == 1", "BA09 == 1", "BA10 == 1", "BA26 == 1 & ATC_A10A == 1"),
keep_data = TRUE)
excl_h2k_women_nonmens##
## ========================================================
## Excluded the following observations:
## ========================================================
## Exclusions based on INCLUSION criteria
##
## inclusion n_prior n_post n_excluded
## 1 Group == 'Women|Non-menstr' 6264 1505 4759
## 2 TOTAL 6264 1505 4759
##
## Exclusions based on EXCLUSION criteria
##
## exclusion n_prior n_post n_excluded
## 1 BMII_PAINO.x < 50 1505 1474 31
## 2 BMII_PAINO.x > 200 1474 1474 0
## 3 Age < 18 1474 1474 0
## 4 Age > 66 1474 1199 275
## 5 SRH >= 4 1199 1066 133
## 6 B_Hb < 125 1066 772 294
## 7 BA08 == 1 772 764 8
## 8 BA09 == 1 764 739 25
## 9 BA10 == 1 739 729 10
## 10 BA26 == 1 & ATC_A10A == 1 729 722 7
## 11 TOTAL 1505 722 783
##
## ========================================================excl_h2k_men <- exclusion_table(data = Health2k,
inclusion_criteria = c("Group == 'Men'"),
exclusion_criteria = c("BMII_PAINO.x < 50", "BMII_PAINO.x > 200", "Age < 18", "Age > 66", "SRH >= 4",
"B_Hb < 135", "BA08 == 1", "BA09 == 1", "BA10 == 1", "BA26 == 1 & ATC_A10A == 1"),
keep_data = TRUE)
excl_h2k_men##
## ======================================================
## Excluded the following observations:
## ======================================================
## Exclusions based on INCLUSION criteria
##
## inclusion n_prior n_post n_excluded
## 1 Group == 'Men' 6264 2944 3320
## 2 TOTAL 6264 2944 3320
##
## Exclusions based on EXCLUSION criteria
##
## exclusion n_prior n_post n_excluded
## 1 BMII_PAINO.x < 50 2944 2938 6
## 2 BMII_PAINO.x > 200 2938 2938 0
## 3 Age < 18 2938 2938 0
## 4 Age > 66 2938 2711 227
## 5 SRH >= 4 2711 2443 268
## 6 B_Hb < 135 2443 2062 381
## 7 BA08 == 1 2062 2023 39
## 8 BA09 == 1 2023 1999 24
## 9 BA10 == 1 1999 1990 9
## 10 BA26 == 1 & ATC_A10A == 1 1990 1970 20
## 11 TOTAL 2944 1970 974
##
## ======================================================Exclusions: FinRisk1997
# ExclusionTable method
excl_fr_women_mens <- exclusion_table(data = FinRisk97,
inclusion_criteria = c("Group == 'Women|Menstr'"),
exclusion_criteria = c("PAINO < 50", "PAINO > 200", "Age < 18", "Age > 66", "SRH >= 4",
"Q15A == 2", "Q16A == 2", "Q17B == 2", "Q17C == 2"),
keep_data = TRUE)
excl_fr_women_mens##
## ====================================================
## Excluded the following observations:
## ====================================================
## Exclusions based on INCLUSION criteria
##
## inclusion n_prior n_post n_excluded
## 1 Group == 'Women|Menstr' 7943 2469 5474
## 2 TOTAL 7943 2469 5474
##
## Exclusions based on EXCLUSION criteria
##
## exclusion n_prior n_post n_excluded
## 1 PAINO < 50 2469 2387 82
## 2 PAINO > 200 2387 2387 0
## 3 Age < 18 2387 2387 0
## 4 Age > 66 2387 2379 8
## 5 SRH >= 4 2379 2265 114
## 6 Q15A == 2 2265 2258 7
## 7 Q16A == 2 2258 2249 9
## 8 Q17B == 2 2249 2223 26
## 9 Q17C == 2 2223 2196 27
## 10 TOTAL 2469 2196 273
##
## ====================================================excl_fr_women_nonmens <- exclusion_table(data = FinRisk97,
inclusion_criteria = c("Group == 'Women|Non-menstr'"),
exclusion_criteria = c("PAINO < 50", "PAINO > 200", "Age < 18", "Age > 66", "SRH >= 4",
"Q15A == 2", "Q16A == 2", "Q17B == 2", "Q17C == 2"),
keep_data = TRUE)
excl_fr_women_nonmens##
## ========================================================
## Excluded the following observations:
## ========================================================
## Exclusions based on INCLUSION criteria
##
## inclusion n_prior n_post n_excluded
## 1 Group == 'Women|Non-menstr' 7943 1463 6480
## 2 TOTAL 7943 1463 6480
##
## Exclusions based on EXCLUSION criteria
##
## exclusion n_prior n_post n_excluded
## 1 PAINO < 50 1463 1430 33
## 2 PAINO > 200 1430 1430 0
## 3 Age < 18 1430 1430 0
## 4 Age > 66 1430 1280 150
## 5 SRH >= 4 1280 1117 163
## 6 Q15A == 2 1117 1100 17
## 7 Q16A == 2 1100 1071 29
## 8 Q17B == 2 1071 1012 59
## 9 Q17C == 2 1012 985 27
## 10 TOTAL 1463 985 478
##
## ========================================================excl_fr_men <- exclusion_table(data = FinRisk97,
inclusion_criteria = c("Group == 'Men'"),
exclusion_criteria = c("PAINO < 50", "PAINO > 200", "Age < 18", "Age > 66", "SRH >= 4",
"Q15A == 2", "Q16A == 2", "Q17B == 2", "Q17C == 2"),
keep_data = TRUE)
excl_fr_men##
## ===========================================
## Excluded the following observations:
## ===========================================
## Exclusions based on INCLUSION criteria
##
## inclusion n_prior n_post n_excluded
## 1 Group == 'Men' 7943 3943 4000
## 2 TOTAL 7943 3943 4000
##
## Exclusions based on EXCLUSION criteria
##
## exclusion n_prior n_post n_excluded
## 1 PAINO < 50 3943 3873 70
## 2 PAINO > 200 3873 3873 0
## 3 Age < 18 3873 3873 0
## 4 Age > 66 3873 3548 325
## 5 SRH >= 4 3548 3190 358
## 6 Q15A == 2 3190 3124 66
## 7 Q16A == 2 3124 3072 52
## 8 Q17B == 2 3072 2987 85
## 9 Q17C == 2 2987 2935 52
## 10 TOTAL 3943 2935 1008
##
## ===========================================Ferritin X CRP
Subgroups: menstruating women, non-menstruating women, men. Grey box highlights all individuals that go over 3 mg/l CRP at ferritin >15 ug/l.
options(scipen = 10000)
ratio1 <- round(nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97")), 2)
ratio2 <- round(nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97")), 2)
ratio3 <- round(nrow(fer_crp %>% filter(Group == "Men" & Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Men" & Cohort == "FinRisk97")), 2)
ratio4 <- round(nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "Health2k")), 2)
ratio5 <- round(nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k")), 2)
ratio6 <- round(nrow(fer_crp %>% filter(Group == "Men" & Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Men" & Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 6), CRP = rep(50, 6), lab = c(paste0(ratio1, "%"), paste0(ratio2, "%"), paste0(ratio3, "%"), paste0(ratio4, "%"), paste0(ratio5, "%"), paste0(ratio6, "%")), Cohort = c(rep("FinRisk97", 3), rep("Health2k", 3)), Group = rep(c("Women|Menstr", "Women|Non-menstr", "Men"), 2))
ggplot(data = fer_crp, aes(x = Ferritin, y = CRP)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 3, ymax = 200, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(alpha = 0.1) +
scale_x_log10() +
scale_y_log10() +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ x, color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(rows = vars(Group), cols = vars(Cohort))
Ferritin X CRP | COLORED
options(scipen = 10000)
ratio1 <- round(nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97")), 2)
ratio2 <- round(nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97")), 2)
ratio3 <- round(nrow(fer_crp %>% filter(Group == "Men" & Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Men" & Cohort == "FinRisk97")), 2)
ratio4 <- round(nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "Health2k")), 2)
ratio5 <- round(nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k")), 2)
ratio6 <- round(nrow(fer_crp %>% filter(Group == "Men" & Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Group == "Men" & Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 6), CRP = rep(50, 6), lab = c(paste0(ratio1, "%"), paste0(ratio2, "%"), paste0(ratio3, "%"), paste0(ratio4, "%"), paste0(ratio5, "%"), paste0(ratio6, "%")), Cohort = c(rep("FinRisk97", 3), rep("Health2k", 3)), Group = rep(c("Women|Menstr", "Women|Non-menstr", "Men"), 2))
ggplot(data = fer_crp, aes(x = Ferritin, y = CRP)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 3, ymax = 200, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(aes(color = Group), alpha = 0.1) +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
geom_smooth(method = "lm", color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
theme(legend.position = "none")
Ferritin X CRP | GROUPS ONLY BY COLOR
options(scipen = 10000)
ratio1 <- round(nrow(fer_crp %>% filter(Cohort == "FinRisk97", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Cohort == "FinRisk97")), 2)
ratio2 <- round(nrow(fer_crp %>% filter(Cohort == "Health2k", CRP >= 3 & Ferritin >= 15)) * 100 / nrow(fer_crp %>% filter(Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 2), CRP = rep(50, 2), lab = c(paste0(ratio1, "%"), paste0(ratio2, "%")), Cohort = c("FinRisk97", "Health2k"))
ggplot(data = fer_crp, aes(x = Ferritin, y = CRP)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 3, ymax = 200, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(aes(color = Group), alpha = 0.2) +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
geom_smooth(method = "lm", color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(cols = vars(Cohort)) +
theme(legend.position = "bottom") + guides(colour = guide_legend(override.aes = list(alpha = 1)))
Proportion analysis code
The estimate: How many more people are over 3 mg/l in CRP when we filter the populations by different ferritin thresholds? Expressed in percentage point difference. The intuition: We currently don’t do ferritin filtering. The population from which we draw donors has a certain proportion of people in risk of cardiac/chronic inflammation, often indicated by elevated hs-CRP (>3 mg/l). Does this proportion increase in a statistically significant manner, if we start requiring higher ferritin levels from donors?
if (!file.exists(paste0("./data/PUBL_finrisk_CRP_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### FinRisk97
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_crp %>% filter(Group == "Men" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = CRP, var1_trld = ferritin_values[i], var2_trld = CRP_trld, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = CRP, var1_trld = ferritin_values[i], var2_trld = CRP_trld, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = CRP, var1_trld = ferritin_values[i],var2_trld = CRP_trld, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_finrisk_CRP_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_finrisk_CRP_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
if (!file.exists(paste0("./data/PUBL_health2k_CRP_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### Health2000
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_crp %>% filter(Group == "Men" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = CRP, var1_trld = ferritin_values[i], var2_trld = CRP_trld, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_crp %>% filter(Group == "Women|Menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = CRP, var1_trld = ferritin_values[i], var2_trld = CRP_trld, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_crp %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = CRP, var1_trld = ferritin_values[i], var2_trld = CRP_trld, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_health2k_CRP_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_health2k_CRP_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
means_all <- rbind(means_finrisk, means_health2k)
means_all$Cohort <- c(rep("FinRisk97", 153), rep("Health2k", 153))
means_all$Group <- factor(means_all$Gender, levels = c("Women|Menstr", "Women|Non-menstr", "Men"))Proportion analysis plot
ggplot(data = means_all, aes(x = Ferritin, y = means)) +
geom_ribbon(aes(ymin = lower, ymax = upper), alpha = .3) +
geom_line(aes(linetype = Group)) +
theme_minimal() +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
labs(y = "%p") + guides(linetype = "none")
Checking statistical significance at points of interest
IMPORTANT: We are checking for non-overlaps of 95% confidence intervals. These intervals have been derived from 10000 bootstrap samples using normal approximation. Normal approximation relies on the assumption, that our bootstrapped estimates are normally distributed, which we have confirmed separately using both visual assessment (a histogram) and a standard Shapiro-Wilk test.
# FINRISK
# Menstruating Women
frwomenpre5 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Menstr" & Ferritin == 5)
frwomenpre15 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Menstr" & Ferritin == 15)
frwomenpre30 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Menstr" & Ferritin == 30)
frwomenpre50 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Menstr" & Ferritin == 50)
# Non-menstruating Women
frwomenpost5 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Non-menstr" & Ferritin == 5)
frwomenpost15 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Non-menstr" & Ferritin == 15)
frwomenpost30 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Non-menstr" & Ferritin == 30)
frwomenpost50 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Women|Non-menstr" & Ferritin == 50)
# Men
frmen5 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Men" & Ferritin == 5)
frmen15 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Men" & Ferritin == 15)
frmen30 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Men" & Ferritin == 30)
frmen50 <- means_all %>% filter(Cohort == "FinRisk97" & Group == "Men" & Ferritin == 50)
# H2K
# Menstruating Women
h2kwomenpre5 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Menstr" & Ferritin == 5)
h2kwomenpre15 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Menstr" & Ferritin == 15)
h2kwomenpre30 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Menstr" & Ferritin == 30)
h2kwomenpre50 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Menstr" & Ferritin == 50)
# Non-menstruating Women
h2kwomenpost5 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Non-menstr" & Ferritin == 5)
h2kwomenpost15 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Non-menstr" & Ferritin == 15)
h2kwomenpost30 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Non-menstr" & Ferritin == 30)
h2kwomenpost50 <- means_all %>% filter(Cohort == "Health2k" & Group == "Women|Non-menstr" & Ferritin == 50)
# Men
h2kmen5 <- means_all %>% filter(Cohort == "Health2k" & Group == "Men" & Ferritin == 5)
h2kmen15 <- means_all %>% filter(Cohort == "Health2k" & Group == "Men" & Ferritin == 15)
h2kmen30 <- means_all %>% filter(Cohort == "Health2k" & Group == "Men" & Ferritin == 30)
h2kmen50 <- means_all %>% filter(Cohort == "Health2k" & Group == "Men" & Ferritin == 50)Using these objects, we can check the significance of between the points of interest. For example “h2kwomenpre5\(upper < h2kwomenpre15\)lower” would evaluate to TRUE, if the proportion of menstruating women over 3 mg/l CRP is significantly higher in the population filtered by ferritin 15 ug/l than 5 ug/l. Indeed, for menstruating women, the proportion is significantly higher at filter levels of 15, 30, and 50 ug/l when compared with 5 ug/l. This holds for non-menstruating women also, except for the FinRisk 1997 cohort, where the difference between levels 5 and 15 was not significant. The differences are all significant also in men, but the respective increases in proportions are much smaller.
Proportion analysis plot | COLORED
ggplot(data = means_all, aes(x = Ferritin, y = means)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Group), alpha = .3) +
geom_line(aes(linetype = Group)) +
#geom_point(aes(color = Group)) +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
labs(y = "%p") +
theme(legend.position = "none")
Proportion analysis plot | COLORED & ALL-IN-1
ggplot(data = means_all, aes(x = Ferritin, y = means, group = Group)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Group), alpha = .3) +
geom_line(aes(linetype = Group)) +
#geom_point(aes(color = Group, shape = Group)) +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
facet_grid(cols = vars(Cohort)) +
labs(y = "%p") +
guides(fill = "none") +
theme(legend.position = "bottom")
Additional markers (for the supplement)
We’ll also take a look at acetylated glycoprotein measurements, glucated haemoglobin, and apolipoproteins A1 and B.
# mastersets for the Supplement
# GlycA
fer_glyca <- bind_rows(FinRisk97 = donor_eligible_fr[, c("Ferritin", "Group", "GlycA")],
Health2k = donor_eligible_h2k[, c("Ferritin", "Group", "GlycA")], .id = "Cohort") %>%
mutate(Group = ordered(Group, levels = c("Women|Menstr", "Women|Non-menstr", "Men")),
Cohort = ordered(Cohort, levels = c("FinRisk97", "Health2k"))) %>%
filter(Group != "NA") %>%
drop_na()
# HbA1C
fer_hba1c <- bind_rows(Health2k = donor_eligible_h2k[, c("Ferritin", "Group", "HbA1C")], .id = "Cohort") %>%
mutate(Group = ordered(Group, levels = c("Women|Menstr", "Women|Non-menstr", "Men")),
Cohort = ordered(Cohort, levels = c("Health2k"))) %>%
filter(Group != "NA") %>%
drop_na()
# APOB
fer_apob <- bind_rows(FinRisk97 = donor_eligible_fr[, c("Ferritin", "Group", "APOB")],
Health2k = donor_eligible_h2k[, c("Ferritin", "Group", "APOB")], .id = "Cohort") %>%
mutate(Group = ordered(Group, levels = c("Women|Menstr", "Women|Non-menstr", "Men")),
Cohort = ordered(Cohort, levels = c("FinRisk97", "Health2k"))) %>%
filter(Group != "NA") %>%
drop_na()
# APOA1
fer_apoa1 <- bind_rows(FinRisk97 = donor_eligible_fr[, c("Ferritin", "Group", "APOA1")],
Health2k = donor_eligible_h2k[, c("Ferritin", "Group", "APOA1")], .id = "Cohort") %>%
mutate(Group = ordered(Group, levels = c("Women|Menstr", "Women|Non-menstr", "Men")),
Cohort = ordered(Cohort, levels = c("FinRisk97", "Health2k"))) %>%
filter(Group != "NA") %>%
drop_na()Mock Cohort|Var Tables (Supplement)
suptable1 <- as.data.frame(table(fer_glyca$Group, fer_glyca$Cohort))
suptable1$GlycA <- c(paste0(round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_glyca$GlycA[fer_glyca$Group == "Men" & fer_glyca$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Men" & fer_glyca$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Men" & fer_glyca$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_glyca$GlycA[fer_glyca$Group == "Men" & fer_glyca$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Men" & fer_glyca$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_glyca$GlycA[fer_glyca$Group == "Men" & fer_glyca$Cohort == "Health2k"])[5], 2), ")"))
suptable1$FER <- c(paste0(round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Men" & fer_glyca$Cohort == "FinRisk97"])[3], 2), " | (", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Men" & fer_glyca$Cohort == "FinRisk97"])[2], 2), ", ", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Men" & fer_glyca$Cohort == "FinRisk97"])[5], 2), ")"),
paste0(round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Menstr" & fer_glyca$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Women|Non-menstr" & fer_glyca$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Men" & fer_glyca$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Men" & fer_glyca$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_glyca$Ferritin[fer_glyca$Group == "Men" & fer_glyca$Cohort == "Health2k"])[5], 2), ")"))
suptable2 <- as.data.frame(table(fer_hba1c$Group))
suptable2$HbA1C <- c(paste0(round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Women|Menstr" & fer_hba1c$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Women|Menstr" & fer_hba1c$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Women|Menstr" & fer_hba1c$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Women|Non-menstr" & fer_hba1c$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Women|Non-menstr" & fer_hba1c$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Women|Non-menstr" & fer_hba1c$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Men" & fer_hba1c$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Men" & fer_hba1c$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_hba1c$HbA1C[fer_hba1c$Group == "Men" & fer_hba1c$Cohort == "Health2k"])[5], 2), ")"))
suptable2$FER <- c(paste0(round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Women|Menstr" & fer_hba1c$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Women|Menstr" & fer_hba1c$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Women|Menstr" & fer_hba1c$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Women|Non-menstr" & fer_hba1c$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Women|Non-menstr" & fer_hba1c$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Women|Non-menstr" & fer_hba1c$Cohort == "Health2k"])[5], 2), ")"),
paste0(round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Men" & fer_hba1c$Cohort == "Health2k"])[3], 2), " | (", round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Men" & fer_hba1c$Cohort == "Health2k"])[2], 2), ", ", round(summary(fer_hba1c$Ferritin[fer_hba1c$Group == "Men" & fer_hba1c$Cohort == "Health2k"])[5], 2), ")"))
suptable1## Var1 Var2 Freq GlycA FER
## 1 Women|Menstr FinRisk97 2045 1.28 | (1.17, 1.42) 23.9 | (12.3, 42.5)
## 2 Women|Non-menstr FinRisk97 1002 1.38 | (1.26, 1.53) 55.79 | (31.42, 94.53)
## 3 Men FinRisk97 2821 1.39 | (1.26, 1.56) 111.37 | (66.46, 181.9)
## 4 Women|Menstr Health2k 1108 1.09 | (0.95, 1.23) 27.6 | (14.8, 48.23)
## 5 Women|Non-menstr Health2k 834 1.15 | (1, 1.3) 55.8 | (31.42, 94.36)
## 6 Men Health2k 1990 1.19 | (1.05, 1.35) 120.8 | (75.15, 189.15)suptable2## Var1 Freq HbA1C FER
## 1 Women|Menstr 1111 31.15 | (28.96, 33.34) 27.6 | (14.8, 48.17)
## 2 Women|Non-menstr 836 33.34 | (31.15, 36.61) 55.8 | (31.48, 94.51)
## 3 Men 1997 34.43 | (32.24, 36.61) 120.8 | (75.5, 189.2)# TODO: suptable3
# TODO: suptable4Ferritin X GlycA | COLORED
options(scipen = 10000)
ratio1 <- round(nrow(fer_glyca %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97", GlycA >= 1.35 & Ferritin >= 15)) * 100 / nrow(fer_glyca %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97")), 2)
ratio2 <- round(nrow(fer_glyca %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97", GlycA >= 1.35 & Ferritin >= 15)) * 100 / nrow(fer_glyca %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97")), 2)
ratio3 <- round(nrow(fer_glyca %>% filter(Group == "Men" & Cohort == "FinRisk97", GlycA >= 1.35 & Ferritin >= 15)) * 100 / nrow(fer_glyca %>% filter(Group == "Men" & Cohort == "FinRisk97")), 2)
ratio4 <- round(nrow(fer_glyca %>% filter(Group == "Women|Menstr" & Cohort == "Health2k", GlycA >= 1.35 & Ferritin >= 15)) * 100 / nrow(fer_glyca %>% filter(Group == "Women|Menstr" & Cohort == "Health2k")), 2)
ratio5 <- round(nrow(fer_glyca %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k", GlycA >= 1.35 & Ferritin >= 15)) * 100 / nrow(fer_glyca %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k")), 2)
ratio6 <- round(nrow(fer_glyca %>% filter(Group == "Men" & Cohort == "Health2k", GlycA >= 1.35 & Ferritin >= 15)) * 100 / nrow(fer_glyca %>% filter(Group == "Men" & Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 6), GlycA = rep(2.2, 6), lab = c(paste0(ratio1, "%"), paste0(ratio2, "%"), paste0(ratio3, "%"), paste0(ratio4, "%"), paste0(ratio5, "%"), paste0(ratio6, "%")), Cohort = c(rep("FinRisk97", 3), rep("Health2k", 3)), Group = rep(c("Women|Menstr", "Women|Non-menstr", "Men"), 2))
ggplot(data = fer_glyca, aes(x = Ferritin, y = GlycA)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 1.35, ymax = 3.7, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(aes(color = Group), alpha = 0.1) +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
geom_smooth(method = "lm", color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
theme(legend.position = "none")
Proportion analysis code (GlycA)
For the acetylated glycoprotein we don’t have any well established thresholds for “healthy” and “unhealthy”. We’ll use the population median here.
iterations <- length(ferritin_values)
if (!file.exists(paste0("./data/PUBL_finrisk_GlycA_", boot_n, ".rds"))) { # run bootstrap only if needed
GlycA_trld <- median(fer_glyca[fer_glyca$Cohort == "FinRisk97", "GlycA"][[1]], na.rm = T)
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### FinRisk97
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_glyca %>% filter(Group == "Men" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = GlycA, var1_trld = ferritin_values[i], var2_trld = GlycA_trld, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_glyca %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = GlycA, var1_trld = ferritin_values[i], var2_trld = GlycA_trld, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_glyca %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = GlycA, var1_trld = ferritin_values[i], var2_trld = GlycA_trld, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_finrisk_GlycA_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_finrisk_GlycA_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
if (!file.exists(paste0("./data/PUBL_health2k_GlycA_", boot_n, ".rds"))) { # run bootstrap only if needed
GlycA_trld <- median(fer_glyca[fer_glyca$Cohort == "Health2k", "GlycA"][[1]], na.rm = T)
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### Health2000
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_glyca %>% filter(Group == "Men" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = GlycA, var1_trld = ferritin_values[i], var2_trld = GlycA_trld, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_glyca %>% filter(Group == "Women|Menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = GlycA, var1_trld = ferritin_values[i], var2_trld = GlycA_trld, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_glyca %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = GlycA, var1_trld = ferritin_values[i], var2_trld = GlycA_trld, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_health2k_GlycA_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_health2k_GlycA_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
means_all <- rbind(means_finrisk, means_health2k)
means_all$Cohort <- c(rep("FinRisk97", 153), rep("Health2k", 153))
means_all$Group <- factor(means_all$Gender, levels = c("Women|Menstr", "Women|Non-menstr", "Men"))Proportion analysis plot (GlycA)
ggplot(data = means_all, aes(x = Ferritin, y = means)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Group), alpha = .3) +
geom_line(aes(linetype = Group)) +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
labs(y = "%p") + theme(legend.position = "none")
Ferritin X HbA1C | COLORED
options(scipen = 10000)
ratio4 <- round(nrow(fer_hba1c %>% filter(Group == "Women|Menstr" & Cohort == "Health2k", HbA1C >= 42 & Ferritin >= 15)) * 100 / nrow(fer_hba1c %>% filter(Group == "Women|Menstr" & Cohort == "Health2k")), 2)
ratio5 <- round(nrow(fer_hba1c %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k", HbA1C >= 42 & Ferritin >= 15)) * 100 / nrow(fer_hba1c %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k")), 2)
ratio6 <- round(nrow(fer_hba1c %>% filter(Group == "Men" & Cohort == "Health2k", HbA1C >= 42 & Ferritin >= 15)) * 100 / nrow(fer_hba1c %>% filter(Group == "Men" & Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 3), HbA1C = rep(60, 3), lab = c(paste0(ratio4, "%"), paste0(ratio5, "%"), paste0(ratio6, "%")), Cohort = rep("Health2k", 3), Group = rep(c("Women|Menstr", "Women|Non-menstr", "Men"), 2))
ggplot(data = fer_hba1c, aes(x = Ferritin, y = HbA1C)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 42, ymax = 100, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(aes(color = Group), alpha = 0.1) +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
geom_smooth(method = "lm", color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(rows = vars(Group)) +
theme(legend.position = "none") +
labs(y = expression(HbA[1*C]))
Proportion analysis code (HbA1C)
The reference values for healthy people are between 20 - 42 mmol/mol (https://www.terveyskirjasto.fi/snk03092). Because the relationship with ferritin appears to be positive, we’ll use the upper bound.
iterations <- length(ferritin_values)
HbA1C_trld <- 42
if (!file.exists(paste0("./data/PUBL_health2k_HbA1C_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### Health2000
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_hba1c %>% filter(Group == "Men" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = HbA1C, var1_trld = ferritin_values[i], var2_trld = HbA1C_trld, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_hba1c %>% filter(Group == "Women|Menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = HbA1C, var1_trld = ferritin_values[i], var2_trld = HbA1C_trld, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_hba1c %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = HbA1C, var1_trld = ferritin_values[i], var2_trld = HbA1C_trld, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_health2k_HbA1C_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_health2k_HbA1C_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
means_all <- means_health2k
means_all$Cohort <- c(rep("Health2k", 153))
means_all$Group <- factor(means_all$Gender, levels = c("Women|Menstr", "Women|Non-menstr", "Men"))Proportion analysis plot (HbA1C)
ggplot(data = means_all, aes(x = Ferritin, y = means)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Group), alpha = .3) +
geom_line(aes(linetype = Group)) +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
facet_grid(rows = vars(Group)) +
labs(y = "%p") + theme(legend.position = "none")
Ferritin X APOB | COLORED
options(scipen = 10000)
ratio1 <- round(nrow(fer_apob %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97", APOB >= 1.3 & Ferritin >= 15)) * 100 / nrow(fer_apob %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97")), 2)
ratio2 <- round(nrow(fer_apob %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97", APOB >= 1.3 & Ferritin >= 15)) * 100 / nrow(fer_apob %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97")), 2)
ratio3 <- round(nrow(fer_apob %>% filter(Group == "Men" & Cohort == "FinRisk97", APOB >= 1.3 & Ferritin >= 15)) * 100 / nrow(fer_apob %>% filter(Group == "Men" & Cohort == "FinRisk97")), 2)
ratio4 <- round(nrow(fer_apob %>% filter(Group == "Women|Menstr" & Cohort == "Health2k", APOB >= 1.3 & Ferritin >= 15)) * 100 / nrow(fer_apob %>% filter(Group == "Women|Menstr" & Cohort == "Health2k")), 2)
ratio5 <- round(nrow(fer_apob %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k", APOB >= 1.3 & Ferritin >= 15)) * 100 / nrow(fer_apob %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k")), 2)
ratio6 <- round(nrow(fer_apob %>% filter(Group == "Men" & Cohort == "Health2k", APOB >= 1.3 & Ferritin >= 15)) * 100 / nrow(fer_apob %>% filter(Group == "Men" & Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 6), APOB = rep(2.2, 6), lab = c(paste0(ratio1, "%"), paste0(ratio2, "%"), paste0(ratio3, "%"), paste0(ratio4, "%"), paste0(ratio5, "%"), paste0(ratio6, "%")), Cohort = c(rep("FinRisk97", 3), rep("Health2k", 3)), Group = rep(c("Women|Menstr", "Women|Non-menstr", "Men"), 2))
ggplot(data = fer_apob, aes(x = Ferritin, y = APOB)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 1.3, ymax = 3, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(aes(color = Group), alpha = 0.1) +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
geom_smooth(method = "lm", color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
theme(legend.position = "none")
Ferritin X APOA1 | COLORED
options(scipen = 10000)
ratio1 <- round(nrow(fer_apoa1 %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97", APOA1 <= 1.2 & Ferritin >= 15)) * 100 / nrow(fer_apoa1 %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97")), 2)
ratio2 <- round(nrow(fer_apoa1 %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97", APOA1 <= 1.2 & Ferritin >= 15)) * 100 / nrow(fer_apoa1 %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97")), 2)
ratio3 <- round(nrow(fer_apoa1 %>% filter(Group == "Men" & Cohort == "FinRisk97", APOA1 <= 1.2 & Ferritin >= 15)) * 100 / nrow(fer_apoa1 %>% filter(Group == "Men" & Cohort == "FinRisk97")), 2)
ratio4 <- round(nrow(fer_apoa1 %>% filter(Group == "Women|Menstr" & Cohort == "Health2k", APOA1 <= 1.2 & Ferritin >= 15)) * 100 / nrow(fer_apoa1 %>% filter(Group == "Women|Menstr" & Cohort == "Health2k")), 2)
ratio5 <- round(nrow(fer_apoa1 %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k", APOA1 <= 1.2 & Ferritin >= 15)) * 100 / nrow(fer_apoa1 %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k")), 2)
ratio6 <- round(nrow(fer_apoa1 %>% filter(Group == "Men" & Cohort == "Health2k", APOA1 <= 1.2 & Ferritin >= 15)) * 100 / nrow(fer_apoa1 %>% filter(Group == "Men" & Cohort == "Health2k")), 2)
ann_text <- data.frame(Ferritin = rep(900, 6), APOA1 = rep(0.6, 6), lab = c(paste0(ratio1, "%"), paste0(ratio2, "%"), paste0(ratio3, "%"), paste0(ratio4, "%"), paste0(ratio5, "%"), paste0(ratio6, "%")), Cohort = c(rep("FinRisk97", 3), rep("Health2k", 3)), Group = rep(c("Women|Menstr", "Women|Non-menstr", "Men"), 2))
ggplot(data = fer_apoa1, aes(x = Ferritin, y = APOA1)) +
annotate("rect", xmin = 15, xmax = 2000, ymin = 0.4, ymax = 1.2, alpha = .5, fill = "grey") +
geom_text(data = ann_text, aes(label = lab)) +
geom_point(aes(color = Group), alpha = 0.1) +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
geom_smooth(method = "lm", color = "black", linetype = "dashed", size = 0.5) +
stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), p.accuracy = 0.001) +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
theme(legend.position = "none")
Proportion analysis code (APOB)
Apolipoprotein B elevates with the risk of cardiovascular diseases and it correlates positively with ferritin. The reference values for healthy men are 0.6 - 1.5 (of which we’ll use 1.5) and for healthy women they are 0.6 - 1.3 (we’ll use 1.3). (https://huslab.fi/ohjekirja/20705.html)
iterations <- length(ferritin_values)
APOB_trld_men <- 1.5 # for males [0.6, 1.5]
APOB_trld_women <- 1.3 # for females [0.6, 1.3]
if (!file.exists(paste0("./data/PUBL_finrisk_APOB_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### FinRisk97
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_apob %>% filter(Group == "Men" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOB, var1_trld = ferritin_values[i], var2_trld = APOB_trld_men, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_apob %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOB, var1_trld = ferritin_values[i], var2_trld = APOB_trld_women, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_apob %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOB, var1_trld = ferritin_values[i], var2_trld = APOB_trld_women, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_finrisk_APOB_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_finrisk_APOB_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
if (!file.exists(paste0("./data/PUBL_health2k_APOB_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### Health2000
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_apob %>% filter(Group == "Men" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOB, var1_trld = ferritin_values[i], var2_trld = APOB_trld_men, var2_over = T)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_apob %>% filter(Group == "Women|Menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOB, var1_trld = ferritin_values[i], var2_trld = APOB_trld_women, var2_over = T)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_apob %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOB, var1_trld = ferritin_values[i], var2_trld = APOB_trld_women, var2_over = T)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_health2k_APOB_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_health2k_APOB_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
means_all <- rbind(means_finrisk, means_health2k)
means_all$Cohort <- c(rep("FinRisk97", 153), rep("Health2k", 153))
means_all$Group <- factor(means_all$Gender, levels = c("Women|Menstr", "Women|Non-menstr", "Men"))Proportion analysis plot (APOB)
ggplot(data = means_all, aes(x = Ferritin, y = means)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Group), alpha = .3) +
geom_line(aes(linetype = Group)) +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
labs(y = "%p") + theme(legend.position = "none")
Proportion analysis code (APOA1)
Apolipoprotein A1 decreases with the risk of atherosclerosis (peripheral, coronary, brain) and appears to correlate negatively with ferritin. Thus, we will use the lower bounds of the reference intervals for healthy individuals here. These are 1.1 - 2.0 for men and 1.2 - 2.3 for women. (https://huslab.fi/ohjekirja/20705.html)
iterations <- length(ferritin_values)
APOA1_trld_men <- 1.1 # for males [1.1, 2.0]
APOA1_trld_women <- 1.2 # for females [1.2, 2.3]
if (!file.exists(paste0("./data/PUBL_finrisk_APOA1_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### FinRisk97
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_apoa1 %>% filter(Group == "Men" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOA1, var1_trld = ferritin_values[i], var2_trld = APOA1_trld_men, var2_over = F)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_apoa1 %>% filter(Group == "Women|Menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOA1, var1_trld = ferritin_values[i], var2_trld = APOA1_trld_women, var2_over = F)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_apoa1 %>% filter(Group == "Women|Non-menstr" & Cohort == "FinRisk97"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOA1, var1_trld = ferritin_values[i], var2_trld = APOA1_trld_women, var2_over = F)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_finrisk <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_finrisk_APOA1_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_finrisk_APOA1_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
if (!file.exists(paste0("./data/PUBL_health2k_APOA1_", boot_n, ".rds"))) { # run bootstrap only if needed
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
bootobs <- setNames(vector("list", length = 3), c("men", "women_mens", "women_nonmens"))
for (i in 1:iterations) {
#############
#### Health2000
#############
## Compute
# Men
bootobs$men[[i]] <- boot(fer_apoa1 %>% filter(Group == "Men" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOA1, var1_trld = ferritin_values[i], var2_trld = APOA1_trld_men, var2_over = F)
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
# Women|Menstr
bootobs$women_mens[[i]] <- boot(fer_apoa1 %>% filter(Group == "Women|Menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOA1, var1_trld = ferritin_values[i], var2_trld = APOA1_trld_women, var2_over = F)
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
# Women|Non-menstr
bootobs$women_nonmens[[i]] <- boot(fer_apoa1 %>% filter(Group == "Women|Non-menstr" & Cohort == "Health2k"), statistic = get_ratio_boot, R = boot_n,
var1 = Ferritin, var2 = APOA1, var1_trld = ferritin_values[i], var2_trld = APOA1_trld_women, var2_over = F)
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
## Store
# Men
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
# Women|Menstr
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
# Women|Non-menstr
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
# Save
saveRDS(bootobs, paste0("./data/PUBL_health2k_APOA1_", boot_n, ".rds"))
} else {
bootobs <- readRDS(paste0("./data/PUBL_health2k_APOA1_", boot_n, ".rds"))
## Preallocate
# Men
means_men <- 1:iterations
upper_men <- 1:iterations
lower_men <- 1:iterations
# Women|Menstr
means_women_mens <- 1:iterations
upper_women_mens <- 1:iterations
lower_women_mens <- 1:iterations
# Women|Non-menstr
means_women_nonmens <- 1:iterations
upper_women_nonmens <- 1:iterations
lower_women_nonmens <- 1:iterations
for (i in 1:iterations) {
ci_obj_men <- boot.ci(bootobs$men[[i]], type = "norm")
ci_obj_women_mens <- boot.ci(bootobs$women_mens[[i]], type = "norm")
ci_obj_women_nonmens <- boot.ci(bootobs$women_nonmens[[i]], type = "norm")
# Store
means_men[i] <- bootobs$men[[i]]$t0
upper_men[i] <- ci_obj_men$normal[3]
lower_men[i] <- ci_obj_men$normal[2]
means_women_mens[i] <- bootobs$women_mens[[i]]$t0
upper_women_mens[i] <- ci_obj_women_mens$normal[3]
lower_women_mens[i] <- ci_obj_women_mens$normal[2]
means_women_nonmens[i] <- bootobs$women_nonmens[[i]]$t0
upper_women_nonmens[i] <- ci_obj_women_nonmens$normal[3]
lower_women_nonmens[i] <- ci_obj_women_nonmens$normal[2]
}
# Combine
means_health2k <- data.frame(Ferritin = rep(ferritin_values, 3),
means = c(means_men, means_women_mens, means_women_nonmens),
upper = c(upper_men, upper_women_mens, upper_women_nonmens),
lower = c(lower_men, lower_women_mens, lower_women_nonmens),
Gender = c(rep("Men", iterations), rep("Women|Menstr", iterations), rep("Women|Non-menstr", iterations)))
}
means_all <- rbind(means_finrisk, means_health2k)
means_all$Cohort <- c(rep("FinRisk97", 153), rep("Health2k", 153))
means_all$Group <- factor(means_all$Gender, levels = c("Women|Menstr", "Women|Non-menstr", "Men"))Proportion analysis plot (APOA1)
ggplot(data = means_all, aes(x = Ferritin, y = means)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Group), alpha = .3) +
geom_line(aes(linetype = Group)) +
scale_color_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
scale_fill_manual(values = c( "#00BFFF", "#de2d26", "#ff85a2" ),
limits = c( "Men", "Women|Non-menstr", "Women|Menstr" )) +
theme_minimal() +
facet_grid(rows = vars(Group), cols = vars(Cohort)) +
labs(y = "%p") + theme(legend.position = "none")